The concept of supply chain big data
The concept of supply chain big data, "big data" is a particularly large volume, the data is particularly large data sets, many people for the concept of big data such as this are half-understood, then the following for you to introduce the concept of supply chain big data.
The concept of big data in the supply chain1The big data supply chain is actually managing the supply chain with data. It's more about taking the various business systems in the supply chain and going through the data and then making that data relevant to each other.
You can find the relationship between them, so that you can have better control over the materials, production, and logistics in the production chain, so as to improve the flow efficiency and reduce costs.
Let me give you a real-world example from Gree Electric (Wuhu).
Their data analytics are used in four ways: logistics analysis, operational efficiency monitoring, production line monitoring, and quality control.
First, logistics analysis
Through the monitoring of the large screen screen to real-time monitoring of business operations, which link to the first time the problem in the dashboard early warning, the information is effective and timely;
Monitoring inventory in each warehouse in the proportion of materials and inventory.
Operational Efficiency Monitoring
Monitor order quantity fulfillment ratio, picking progress, order quantity and ratio of complete sets;
Monitor production efficiency of each production unit in the production plant, and the ratio of offline.
Third, the production line monitoring
The system data collected by MES and MPR, connected to Yonghong Z-Suite for real-time multi-dimensional analysis.
For example, in the past, it was necessary to conduct point-to-point inspections of the materials, but now the results of the inspections are displayed in real time on the analytics platform, and the indicator system can be flexibly adjusted according to the situation, which has increased the efficiency of the IT staff by more than 30%.
Four, quality control
Before the production process and quality management of the site are manually imported into the system data and then use EXCEL built-in charts for simple analysis.
Now they are starting to combine more business analysis dimensions for exploratory analysis and analysis and forecasting, with the help of a big data analysis platform to realize multiple dimensions from the production line, the team and the sub-factory to show the company's overall production and operation at all levels.
Through the data analysis platform can improve the core competitiveness in the production process, the material, the production process of all-round monitoring, in order to improve the efficiency of work at the same time, but also to reduce the production line defective rate.
In fact, from the application of Gree Electric (Wuhu), we can summarize that data analysis can help the supply chain has two important points:
1, BI to the supply chain of all the data to carry out a comprehensive monitoring;
2, for the production of each step of the production process of the material inventory matching can be adjusted in a timely manner to improve efficiency.
For what degree of supply chain management can be achieved, here is a very vivid image.
No warehousing at all. The means of transportation (e.g., the vehicle) is a mobile mini-warehouse that keeps the warehouse on the road at all times. It's a bit like how container shipping lines manage empty containers, where the empty container yard is not on land, but on the ship, wherever it's needed.
Of course this may be too ideal, but for manufacturing companies, every penny of reduction, multiplied by a huge number, is an astronomical amount.
So applying a data platform to manage the supply chain is essential.
The concept of big data in the supply chain2
What is big data
Big data is actually in accordance with the storage unit, `, we often use M, G.
Transcendental G and above, there is also the T, which we still see some of our daily life, such as hard disk used now.
Then there are PB, EB, ZB, YB, and then there are more, so if you are interested, you can ask your mom.
Ali's seems to be a Pangu system.
The data is like the stars, and in ancient times you could only count with your eyes. Now it can be seen, can not see can be deduced, can also go up to observe what attributes. The technology can be analyzed when it is achieved.
After analyzing these trivial information, you can know that you are on the network is male, female, the main activities in that area, know what you like to buy, know your approximate income and so on. Merchants according to these to find big data analytics company will be able to give you guys to put your attention to the product.
It's a bit like when spies used to increase the price of potatoes to know that the garrison was increased around this.
Concept of Big Data in the Supply Chain3Big Data Supply Chain
As the supply chain becomes more complex, better tools must be employed to quickly and efficiently maximize the value of the data. The supply chain, as the core network chain of the enterprise, will revolutionize the enterprise market boundaries, business portfolio, business model and mode of operation.
The tertiary supply chain collaboration application market has a large entry space, especially in the medical, financial, e-commerce and other segments with high demand. The maturity of the secondary industry supply chain collaboration market is gradually increasing, especially in logistics, automotive, retail, public **** business as the main areas, supply chain collaboration data will play a core driving role in market upgrading.
Whether it is the tertiary industry, or the secondary industry
How do you actually apply big data?
1, forecast
Accurate demand forecasting. Demand forecasting is the source of the entire supply chain, the entire market demand fluctuations of the barometer, sales forecasting sensitivity or not directly related to inventory strategy, production arrangements and the end customer order delivery rate, product out of stock and off-sale will bring huge losses to the enterprise. Enterprises need to be effective qualitative and quantitative forecasting and analysis tools and models and combined with historical demand data and safety stock levels to specify the precise demand forecasting program.
For example, in the automotive industry, after applying a data analytics platform for accurate forecasting, a series of information can be collected in a timely manner, such as when to sell, when to break down, and when to warranty, so as to optimize the design, development, manufacturing, demand forecasting, aftermarket, and logistic management, to improve efficiency and bring a better user experience to customers.
2. Resource acquisition
Agile and transparent sourcing and procurement. Find new qualified suppliers to meet production needs for new products and optimize costs; at the same time, through supplier performance evaluation and contract management, make the procurement process standardized, standardized, visualized and cost-optimized.
3, synergistic efficiency
The establishment of good supplier relations, the realization of the two sides of the information interaction. Good supplier relationships are the key to eliminating the cost of distrust between suppliers and manufacturers. The interaction of inventory and demand information between the two sides, and the establishment of VMI operation mechanism will reduce the production loss caused by out-of-stock. The ability to respond quickly and accurately to purchase orders and production orders through various channels is especially important in the current environment of grouping, globalization, and multi-organization operations. The speed of order processing can reflect the efficiency of the supply chain to a certain extent.
4, supply chain planning, and material orders synchronized production planning and scheduling
Effective supply chain planning system integrates all the planning and decision-making business, including demand forecasting, inventory planning, resource allocation, equipment management, channel optimization, production operations planning, material requirements and procurement planning.
Enterprises prepare production plans and schedules based on the capacity of multiple factories to ensure that the production process is orderly and uniform, including the decomposition of material supply and the splitting of production orders. In this process, companies need to balance the relationship between orders, capacity, scheduling, inventory, and cost, and require extensive mathematical modeling, optimization, and simulation techniques to find optimal solutions to complex production and supply problems.
5. Inventory optimization
Mature replenishment and inventory coordination mechanisms eliminate excess inventory and reduce inventory holding costs. By considering from the demand change, safety stock level, procurement lead time, maximum stock setting, procurement ordering lot, procurement change, etc., supervise the optimized inventory structure and stock level setting.
6, logistics efficiency
Establish efficient transportation and distribution center management, through big data analysis of reasonable transportation management, road capacity resource management, build the visualization of the whole business process, reasonable distribution center of the transfer of goods between the correct selection and management of outsourcing carriers and their own fleet, to improve the business risk management and control, and improve business operations. The company's ability to manage and control business risks and improve business operations and customer service quality is also improved.
7. Network Design and Optimization
For investment and expansion, companies analyze costs, capacity, and changes from a supply chain perspective in a more intuitive, informative, and rational way. Companies need to apply enough scenario analysis and dynamic cost optimization models to help them make distribution consolidation and line setup decisions.
8, the manufacturing industry management features prominent in the supply chain management industry management differences
Such as the automotive industry focus on on-time on-line and distribution links, the food and beverage industry focus on the cold chain and distribution links, the apparel industry's supply chain management difficulties in the elimination of the chain of high inventory and so on.
9, risk warning in the supply chain management industry management differences
In big data and predictive analytics, there are a large number of supply chain opportunities. For example, problem prediction can prepare a solution before a problem arises to avoid being caught off guard and causing an operational disaster.
It can also be applied to quality risk control, such as Shanghai Baosteel, whose production lines are all streamlined, and the sensors on the production line have access to a large amount of real-time data, which can be used to effectively control product quality. Through the collection of a large number of data on the production line to determine the health of the equipment operating conditions, the equipment failure time and probability of prediction. This allows companies to schedule equipment maintenance in advance to ensure production safety.
Big data will be used in the supply chain from demand generation, product design to procurement, manufacturing, orders, logistics, and coordination of all aspects of the supply chain through the use of big data to informative control of their supply chain, a clearer grasp of inventory, order fulfillment rate, materials and product distribution, etc.; through advance data analysis to regulate supply and demand; the use of new planning to optimize supply chain strategy and network, to promote the development of core competencies of enterprises. The company's core competency is the ability to optimize the supply chain and optimize the network.
How do companies deploy big data?
The first step in making data valuable is to process big data, to be able to ****enjoy, integrate, store, and search huge amounts of data from many sources. And in the case of the supply chain, that means being able to accept data from third-party systems and speed up feedback. The overall impact is increased collaboration, faster decision making and greater transparency, which helps all involved.
Traditional supply chains already use large amounts of structured data, and organizations deploy advanced supply chain management systems that store resource data, transaction data, supplier data, quality data, and more to track supply chain execution efficiency, costs, and control product quality.
The application of big data in the supply chain field started not long ago, with the rapid development of the supply chain, big data analytics, data management, big data applications, big data storage in the supply chain contains a huge potential for development, big data investment can only be combined with the supply chain in order to generate sustainable, large-scale development of the industry.